Paraphrase identification using weighted dependencies and word semantics
In this paper we propose a novel approach to the task of paraphrase identification. The proposed approach quantifies both the similarity and dissimilarity between two sentences. The similarity and dissimilarity is assessed based on lexicosemantic information, i.e., word semantics, and syntactic information in the form of dependencies, which are explicit syntactic relations between words in a sentence. Word semantics requires mapping words onto concepts in a taxonomy and then using word-to-word similarity metrics to compute their semantic relatedness. Dependencies are obtained using state-of-the-art dependency parsers. One important aspect of our approach is the weighting of missing dependencies, i.e., syntactic relations present in one sentence but not the other. We report experimental results on the Microsoft Paraphrase Corpus, a standard data set for evaluating approaches to paraphrase identification. The experiments showed that the proposed approach offers state-of-the-art results. In particular, our approach offers better precision when compared to other state-of-the-art systems. Copyright © 2009, Assocation for the Advancement of ArtdicaI Intelligence (www.aaai.org). All rights reserved.
Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22
Lintean, M., & Rus, V. (2009). Paraphrase identification using weighted dependencies and word semantics. Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22, 260-265. Retrieved from https://digitalcommons.memphis.edu/facpubs/3057